EJNMMI Physics最新文献

Background: Accurate cellular dosimetry is essential to investigate fundamental mechanisms of targeted radionuclide therapy. The aim of this study was to assess how morphological cellular geometry models influence cellular dosimetry estimates, in comparison to simplified spherical models that do not properly represent an adherent cell geometry.

Methods: Virtual cell models of the CA20948 cell line were generated by confocal microscopy of SSTR2 and DAPI staining and served as input to derive morphological S-values for ¹⁷⁷Lu and ¹⁶¹Tb. Absorbed dose-response relationships were established for [¹⁷⁷Lu]Lu-DOTA-TATE, [¹⁶¹Tb]Tb-DOTA-TATE, [¹⁷⁷Lu]Lu-DOTA-LM3 and [¹⁶¹Tb]Tb-DOTA-LM3 using S-values from both morphological and spherical cell geometries.

Results: Thirty-four cell geometries were modeled and a spherical model with equivalent volume was generated with a radius for the cell and nucleus of 8.6(7) µm and 5.5(6) µm, respectively. Compared to spherical cell models, morphological cell models significantly changed the S-value with an increase of 13% (¹⁷⁷Lu) and 22% (¹⁶¹Tb) with the cell membrane as source region and a decrease of 11% (¹⁷⁷Lu) and 12% (¹⁶¹Tb) with the cytoplasm as source region. Absorbed dose-response relationships based on morphological cell geometries showed a linear dose-response model for [¹⁷⁷Lu]Lu-DOTA-TATE and [¹⁶¹Tb]Tb-DOTA-TATE with α = 0.22[0.18,0.26] Gy-1, and a linear-quadratic dose-response model for [¹⁷⁷Lu]Lu-DOTA-LM3 and [¹⁶¹Tb]Tb-DOTA-LM3 with α = 0.000[0.000,0.022] Gy-1 and β = 0.064[0.055,0.072] Gy-2. The assumption of a spherical cell model did not significantly affect the dose-response models, while underestimating the cell dimensions did induce a rescaling of the dose-response models.

Conclusion: These findings validate the use of simplified spherical models for CA20948 cells but highlight the importance of a correct estimation of the cell dimensions.

Background: Interpreting [¹⁸F]PSMA-1007 PET/CT scans can be challenging due to the occurrence of unspecific uptake in lymph nodes and bones. Machine learning has proven its suitability to use features to model complex relations leading to accurate diagnosis. We aimed to investigate the impact of contextual information on machine learning performance in identifying lymph node and bone metastases in prostate cancer patients using [¹⁸F]PSMA-1007 PET. A Random Forest Classifier (RFC) and Extreme Gradient Boosting (XGBoost) were trained across two feature sets to classify hotspots into malignant or non-malignant. The first set incorporated hotspot-specific features, such as SUVmax, anatomic location and tissue type of the location (lymph node/bone). The second set was the first set combined with context-level features, such as SUVmax of nearby hotspots and the number of hotspots.

Results: We retrospectively included 103 patients who underwent clinically indicated [¹⁸F]PSMA-1007 PET/CT, in whom hotspots were observed in lymph nodes (n = 256) and bone structures (n = 267). The context-enhanced model outperformed the hotspot-specific model in Area Under The Curve (AUC) and Youden Index for both RFC and XGBoost. The context-enhanced RFC performed superior in AUC for bone (0.92, p < 0.001) and lymph node hotspots (0.95, p < 0.001). The performance increase after adding contextual information was stronger for bone hotspots compared to lymph node hotspots in terms of AUC (0.06 vs. 0.03, p < 0.001) and Youden Index (0.15 vs. 0.07, p < 0.001).

Conclusion: We successfully developed models to accurately identify lymph node and bone metastases. We underscored the potential of leveraging contextual information in machine learning methods to improve the identification of lymph node and bone metastases.

Purpose: We applied a voxel-based personalized dosimetry method using multiple voxel S-values (VSVs) to assess the intratumoral dose distribution in hepatocellular carcinoma (HCC) patients treated with trans-arterial radioembolization (TARE) using glass microspheres. This study aimed to evaluate the predictive value of dose metrics for treatment response and local progression-free survival (L-PFS).

Methods: Ninety patients with HCC who underwent TARE between November 2015 and December 2019 were retrospectively analyzed. Post-treatment ⁹⁰Y-microsphere PET/CT images were used to generate voxel-wise absorbed dose maps via convolution with CT-derived multiple VSV kernels. Tumor volumes were manually delineated on contrast-enhanced CT co-registered with dose map. Dose-volume histogram (DVH) parameters, including V-V₂₀₅ (tumor volume receiving < 205 Gy) and D70, were calculated. Dose heterogeneity was assessed using the coefficient of variation (CoV) of voxel doses. The performance of dose parameters for predicting complete response (CR) and L-PFS was evaluated using logistic and Cox regression analyses.

Results: Among 90 patients, 57 (63.3%) achieved CR. PET average dose, D70, V-V₂₀₅, and CoV significantly differed according to treatment response. ROC analysis showed good predictive performance for CR with D70 (AUC 0.908), V-V₂₀₅ (0.903), PET average dose (0.876), and CoV (0.870). In multivariate logistic regression, only small V-V₂₀₅ (< 22.8 mL; OR 12.50, P = 0.002) remained an independent predictor of CR. For L-PFS, multivariate Cox analysis identified V-V₂₀₅ < 22.8 mL (HR 0.07, P = 0.013) and CR (HR 0.29, P = 0.034) as independent prognostic factors.

Conclusion: Voxel-based dosimetry using multiple VSV kernels enables quantitative assessment of intratumoral dose distribution in HCC patients treated with TARE. Among voxel-level parameters, D70, V-V₂₀₅, and CoV showed good performance in predicting CR, and V-V₂₀₅ was the only independent predictor for both treatment response and L-PFS. These findings support the added prognostic value of voxel-based dose metrics beyond average tumor absorbed dose.

Background: We evaluate the Omni Legend 32 cm (OMNI6R), a digital-BGO PET/CT using the deep learning (DL) based algorithm, Precision Deep Learning (PDL), emulating time-of-flight (TOF) enhancement and compare its performance to the TOF-equipped Discovery-MI 25 cm (DMI5R) in terms of detection sensitivity, quantification, and overall image quality.

Methods: Thirty patients were administered with an average single dose of 2 MBq/kg [¹⁸F]-FDG and were scanned consecutively on DMI5R first and on OMNI6R afterwards. Total scan duration on DMI5R and OMNI6R were 10 and 6 min, respectively. OMNI6R data were reconstructed using Bayesian Penalized Likelihood (BPL) algorithm with a beta of 650 and PDL-High setting. A total of 150 inserted synthetic lesions (ISL), ranging in size from 6 to 10 mm and exhibiting contrast levels between 3 and 15 relative to their initial background activity, were distributed across the cohort. Three readers blindly assessed detection sensitivity and quantification of these lesions. We tested a non-inferiority hypothesis based on the ISL true positive rate (TPR) and compared calculated recovery coefficients (RC) using SUVmean and SUVmax metrics of the detected ISL. Additionally, image quality, sharpness, conspicuity, noise characteristics, and diagnostic confidence were assessed as clinical quality indicators with a 5-point Likert scale on clinical images without ISL, using same beta as DMI5R and different PDL settings (None, High, Medium, Low).

Results: TPR were 84.67% (95% CI 80.04-89.29%) and 84.44% (95% CI 77.76-91.13%) respectively for DMI5R and OMNI6R-PDL-High, and demonstrated non-inferiority. OMNI6R-PDL-High yielded higher RC without overestimation for all ISL sizes. Remarkably, these findings were observed despite a 9% activity decay in ISL and a 40% reduction in whole-body acquisition time. All PDL settings led to increased average median scores across clinical quality metrics, surpassing the DMI5R in most cases.

Conclusions: OMNI6R using PDL-High demonstrated non-inferior diagnostic performance compared to DMI5R, as evidenced by ISL detection sensitivity and quantitation. Importantly, the use of OMNI-PDL-High did not increase the risk of false-negative findings, despite reductions in activity and acquisition time. OMNI6R using PDL enhances overall image quality while improving clinical workflow and patient comfort. These results support DL-based enhancement algorithms as effective solutions for non-TOF PET imaging. Trial registration number and date of registration: NCT05154877, December 13th 2021.

Background: Head motion during brain positron emission tomography (PET) degrades image quality and quantitative accuracy. Therefore, a data-driven motion correction (MC) method utilizing ultrafast list-mode reconstruction technology has been proposed and shown to considerably improve image quality. However, reproducing accurate actual motions and motion-free images using clinical data alone remains challenging. This study aimed to quantitatively evaluate data-driven MC using a brain phantom for known tracer distributions and a custom-made motion generator system for variable known motions.

Methods: Hoffman 3D brain phantom was filled with 20 and 3 MBq of [¹⁸F]fluoro-2-deoxy-D-glucose (FDG) to simulate high- and low-radioactivity conditions corresponding to brain FDG PET and amyloid PET acquisitions, respectively. Two separate phantom measurements were performed accordingly. Motion simulation was conducted using a custom-designed motion generator, incorporating 15° and 30° rotations about the z-axis, 3° and 6° rotations about the x-axis, and 5 mm and 10 mm translations along the z-axis in the PET image coordinates. The data-driven MC was applied with frame durations of 1, 2, 5, 10, and 20 s for motion estimation. The estimated motions were compared with the motions measured using an external optical tracker system. %contrast and gray matter coefficient of variation (CV%) were calculated from the motion-corrected PET images.

Results: The motion generator system successfully reproduced the designed motions. Motion estimation remained stable under high-radioactivity condition but showed reduced stability under low-radioactivity condition, particularly with shorter frame durations. Under both conditions, longer frame durations led to underestimation of continuous motion. The data-driven MC improved %contrast and gray matter CV% across all conditions, with shorter frame durations providing better correction for quick or continuous motions. However, shorter frame durations increased statistical noise, especially under low-radioactivity condition.

Conclusion: The data-driven MC effectively improved the quality of motion-affected PET images under both high- and low-radioactivity conditions, indicating its broad applicability. However, correction accuracy deteriorated under the lower-radioactivity condition.

Introduction: ACTIVE 7 MAX is a compact benchtop preclinical PET scanner dedicated to high sensitivity and high-resolution imaging of small animals. This study evaluated the performance of the ACTIVE 7 MAX system using the National Electrical Manufacturers Association NU 4-2008 standard protocol.

Methods: The scanner consists of four rings, each containing 14 detector modules. Each detector module is made up of a 16 × 16 array of lutetium yttrium orthosilicate (LYSO) scintillation crystals, with each crystal measuring 1.47 × 1.47 × 10.0 mm³. The crystal array was coupled to a novel 6 × 6 epitaxial-quenching-resistor silicon photomultiplier (EQR SiPM) array. Flood images and energy resolution were obtained by irradiating each detector module with a ¹⁸F source.

Results: The average energy resolution for the 56 detector modules in the system was found to be 11.46% in Full Width at Half Maximum (FWHM). The filtered back-projection (FBP) image spatial resolution of a point source varied from 1.57 to 1.81 mm in the radial direction and from 1.58 to 1.71 mm in the tangential direction within the radius of 25 mm. For the Derenzo phantom imaging, the hot rod with a diameter of 1.25 mm was identified. With an energy window of 350-650 keV, the sensitivity at the center of the scanner was 4.18%. The peak noise equivalent count rate (NECR) of 103 kcps was achieved at 22 MBq, and the scatter fraction (SF) is 12%. The reconstructed images of the NEMA image quality phantom show a uniformity of 5.0%, and recovery coefficients for rods with diameters of 1 mm and 5 mm ranging from 0.14 to 0.93. The spillover rates for air-filled and water-filled cold regions were 0.08 and 0.03, respectively.

Conclusion: This study evaluated the performance of the ACTIVE 7 MAX preclinical PET system. The results demonstrated excellent imaging performance for molecular imaging in biomedical studies.

Purpose: Before utilising preclinical Position Emission Tomography (PET) systems for biological studies, evaluating their performance is important to better qualify the scanner's applications. This study aims to assess the performance of the new extended field of view (FOV) nanoScan® PET/CT P123S system, developed for rodent total-body PET applications.

Methods: Scanner resolution, noise equivalent count rate (NECR), sensitivity and image quality were evaluated following NEMA NU-4 2008 protocols. Furthermore, a Derenzo phantom and linearity measurements were conducted. In vivo studies were subsequently carried out to evaluate system performance in biological applications.

Results: The scanner spatial resolution according to the NEMA protocol was 1.4 mm using FBP reconstruction, while with iterative reconstruction it was under 0.7 mm. The NECR peak using a 250‒750 keV energy window was 1805.0 kcps at 93.7 MBq and 880.7 kcps at 88.4 MBq for the mouse-sized and rat-sized phantom respectively. The absolute sensitivity was 10.5%. The standard deviation of the uniform area of the image quality phantom was 1.8%, while the recovery coefficients varied between 0.23 and 1.00. The spill-over ratios were 0.04, and 0.04 in the water and air-filled chambers respectively. Quantitative bias was < 4% with a linear response up to 105 MBq. Total-body rat images were successfully acquired using the new system.

Conclusion: The new extended FOV PET system has improved sensitivity and count rate performance compared with previous systems. Its spatial resolution and quantitative accuracy are well-suited for preclinical PET applications. The extended FOV enables total-body imaging of both mice and rats.

Purpose: This study aims to develop and validate a dual-time-window (DTW) Patlak plot method that eliminates the need for invasive blood sampling and reduces scan duration. We seek to improve the accuracy of the net influx constant ([Formula: see text]) estimation, addressing the inaccuracies inherent in traditional DTW and single-time-window methods, which often introduce bias and hinder comparability across different cohorts.

Method: We developed an unsupervised, multi-branch neural network (NN) to assist in estimating missing data intervals within the DTW protocol, thereby facilitating accurate Patlak analysis. The model fits the mapping from time to the time-activity curve (TAC), generating multiple pseudo input functions (IFs). A correlation coefficient is then computed between each pseudo IF and the voxel-level measured data, extracting statistical information guided by the kinetic process. These correlation scores were used to construct a weighted statistic, serving as the final IF (NNIF). Our approach was validated using both simulation and clinical data, including [Formula: see text]-FDG PET scans from 67 lung cancer subjects. Additionally, we compared the performance of our method with other simplified quantification techniques to demonstrate its efficacy in achieving high-quality parametric imaging and reliable quantitative analysis within abbreviated scanning protocols.

Result: Our proposed method achieved high accuracy in the estimation of IF, with a maximum mean absolute deviation (MAD) of 0.04 in a real patient study. The regressed [Formula: see text] derived from different DTW scan protocols exhibited good consistency. In simulation studies , the best relative absolute error (RAE) was 0.0302. In real patient study, the optimal average peak signal-to-noise ratio (PSNR) of parametric imaging reached 97.40 dB, while the best average R-squared ([Formula: see text]) in ROI-based quantitative analysis reached 0.991.

Conclusions: We demonstrate the feasibility of using a weighted statistic, constructed from a multi-branch neural network, to accurately estimate the complete IF. This approach enables the generation of high-quality parametric images with shortened scan protocols, effectively reducing scanning time while ensuring accurate Patlak analysis.